{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iceberg Cosine Figures\n",
    "Make tables related to the performance of ICEBERG on cosine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yaml\n",
    "from ms_pred.common.plot_utils import *\n",
    "\n",
    "from collections import defaultdict\n",
    "from scipy.stats import sem\n",
    "\n",
    "\n",
    "set_style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_names = [\"nist20\", \"canopus_train_public\"]\n",
    "\n",
    "# Define\n",
    "# data_folder = Path(f\"../data/spec_datasets/{dataset_name}/\")\n",
    "# labels = data_folder / \"labels.tsv\"\n",
    "results_folder = Path(\"../results/figs_iceberg/weave_acc/\")\n",
    "\n",
    "results_folder.mkdir(exist_ok=True, parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\n",
    "    \"CFM-ID\",\n",
    "    \"3DMolMS\",\n",
    "    \"FixedVocab\",\n",
    "    \"NEIMS (FFN)\",\n",
    "    \"NEIMS (GNN)\",\n",
    "    \"MassFormer\",\n",
    "    \"SCARF\",\n",
    "    \"ICEBERG\",\n",
    "]\n",
    "\n",
    "dataset_to_res = {}\n",
    "for dataset_name in dataset_names:\n",
    "    yaml_files = defaultdict(lambda : [])\n",
    "    for seed in [1,2,3]:    \n",
    "        results_files = [\n",
    "            f\"../results/cfm_id_{dataset_name}/split_1/preds/pred_eval.yaml\",\n",
    "            f\"../results/molnetms_baseline_{dataset_name}/split_1_rnd{seed}/preds/pred_eval.yaml\",\n",
    "            f\"../results/graff_ms_baseline_{dataset_name}/split_1_rnd{seed}/preds/pred_eval.yaml\",\n",
    "            f\"../results/ffn_baseline_{dataset_name}/split_1_rnd{seed}/preds/pred_eval.yaml\",\n",
    "            f\"../results/gnn_baseline_{dataset_name}/split_1_rnd{seed}/preds/pred_eval.yaml\",\n",
    "            f\"../results/massformer_baseline_{dataset_name}/split_1_rnd{seed}/preds/pred_eval.yaml\",\n",
    "            f\"../results/scarf_inten_{dataset_name}/split_1_rnd{seed}/preds/pred_eval.yaml\",\n",
    "            f\"../results/dag_inten_{dataset_name}/split_1_rnd{seed}/preds/pred_eval.yaml\",\n",
    "        ]\n",
    "        for i, j in zip(names, results_files):\n",
    "            yaml_files[i].append(yaml.safe_load(open(j, \"r\")))\n",
    "    dataset_to_res[dataset_name] = yaml_files    \n",
    "\n",
    "    results_files = [\n",
    "        f\"../results/cfm_id_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "        f\"../results/molnetms_baseline_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "        f\"../results/graff_ms_baseline_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "        f\"../results/ffn_baseline_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "        f\"../results/gnn_baseline_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "        f\"../results/massformer_baseline_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "        f\"../results/scarf_inten_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "        f\"../results/dag_inten_{dataset_name}/scaffold_1/preds/pred_eval.yaml\",\n",
    "    ]\n",
    "    yaml_files = defaultdict(lambda : [])\n",
    "    for i, j in zip(names, results_files):\n",
    "        if Path(j).exists():\n",
    "            yaml_files[i].append(yaml.safe_load(open(j, \"r\"))) \n",
    "\n",
    "    if len(yaml_files) > 0:\n",
    "        dataset_to_res[f\"scaffold_{dataset_name}\"] = yaml_files    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_to_time = {\n",
    "    \"CFM-ID\": \"../results/cfm_id_nist20_timer/time_out.json\",\n",
    "    \"3DMolMS\": \"../results/molnetms_baseline_nist20/split_1_rnd1/time_out.json\",\n",
    "    \"FixedVocab\": \"../results/graff_ms_baseline_nist20/split_1_rnd1/time_out.json\",\n",
    "    \"NEIMS (FFN)\": \"../results/ffn_baseline_nist20/split_1_rnd1/time_out.json\",\n",
    "    \"NEIMS (GNN)\": \"../results/gnn_baseline_nist20/split_1_rnd1/time_out.json\",\n",
    "    \"MassFormer\": \"../results/massformer_baseline_nist20/split_1_rnd1/time_out.json\",\n",
    "    \"SCARF\": \"../results/scarf_inten_nist20/split_1_rnd1/time_out.json\",\n",
    "    \"ICEBERG\": \"../results/dag_inten_nist20/split_1_rnd1/time_out.json\",\n",
    "}\n",
    "name_to_seconds = {\n",
    "    i: yaml.safe_load(open(j, \"r\"))[\"time (s)\"] for i, j in name_to_time.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/samlg/.conda/envs/ms-gen/lib/python3.8/site-packages/numpy/core/_methods.py:264: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/samlg/.conda/envs/ms-gen/lib/python3.8/site-packages/numpy/core/_methods.py:256: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "out_df = []\n",
    "for dataset_name, yaml_files in dataset_to_res.items():\n",
    "    for k, v in yaml_files.items():\n",
    "        new_entry = {\n",
    "            \"Method\": k,\n",
    "            \"Cosine sim.\": np.mean([vv['avg_cos_sim'] for vv in v]),\n",
    "            \"Cosine sim. 95%\": 1.96 * sem([vv['avg_cos_sim'] for vv in v])   ,  ###v[\"sem_cos_sim\"],\n",
    "\n",
    "            \"Cosine sim. (no PEP)\": np.mean([vv['avg_cos_sim_zero_pep'] for vv in v]),\n",
    "            \"Cosine sim. 95% (no PEP)\": 1.96 * sem([vv['avg_cos_sim_zero_pep'] for vv in v])   ,  ###v[\"sem_cos_sim\"],\n",
    "\n",
    "            \"Coverage\": np.mean([vv['avg_coverage'] for vv in v]),\n",
    "            \"Coverage 95%\": 1.96 * sem([vv['avg_coverage'] for vv in v])   ,  ###v[\"sem_cos_sim\"],\n",
    "            \n",
    "            \"Valid\": np.mean([vv['avg_frac_valid'] for vv in v]),\n",
    "            \"Valid 95%\": 1.96 * sem([vv['avg_frac_valid'] for vv in v])   ,  ###v[\"sem_cos_sim\"],\n",
    "\n",
    "            \"Time (s) orig\": name_to_seconds[k],\n",
    "            \"Dataset\": dataset_name,\n",
    "        }\n",
    "        out_df.append(new_entry)\n",
    "out_df = pd.DataFrame(out_df)\n",
    "\n",
    "# Replace nan with 0 \n",
    "out_df = out_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rewrite cosine sim column with f\"cos sim \\pm cosine sem using list comprehension\n",
    "\n",
    "out_df[\"Cosine sim. orig\"] = out_df[\"Cosine sim.\"] \n",
    "\n",
    "out_df[\"Cosine sim.\"] = [\n",
    "    rf\"${i:.3f} \\pm {j:.3f}$\"\n",
    "    for i, j in zip(out_df[\"Cosine sim.\"], out_df[\"Cosine sim. 95%\"])\n",
    "]\n",
    "# Rewrite coverage column with f\"coverage \\pm coverage sem using list comprehension\n",
    "out_df[\"Coverage\"] = [\n",
    "    rf\"${i:.3f} \\pm {j:.3f}$\"\n",
    "    for i, j in zip(out_df[\"Coverage\"], out_df[\"Coverage 95%\"])\n",
    "]\n",
    "# Same for valid\n",
    "out_df[\"Valid\"] = [\n",
    "    rf\"${i:.2f} \\pm {j:.3f}$\" for i, j in zip(out_df[\"Valid\"], out_df[\"Valid 95%\"])\n",
    "]\n",
    "out_df[\"Time (s)\"] = [rf\"${i:.1f}$\" for i in out_df[\"Time (s) orig\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\\centering\n",
      "\\caption{Comparing NIST scaffold vs. random spllit cosine similarity}\n",
      "\\label{tab:scaffold}\n",
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "Dataset &           nist20 &  scaffold_nist20 \\\\\n",
      "{} & Cosine sim. orig & Cosine sim. orig \\\\\n",
      "\\midrule\n",
      "CFM-ID      &            0.412 &            0.411 \\\\\n",
      "3DMolMS     &            0.510 &            0.466 \\\\\n",
      "FixedVocab  &            0.704 &            0.658 \\\\\n",
      "NEIMS (FFN) &            0.617 &            0.546 \\\\\n",
      "NEIMS (GNN) &            0.694 &            0.643 \\\\\n",
      "MassFormer  &            0.721 &            0.682 \\\\\n",
      "SCARF       &            0.726 &            0.669 \\\\\n",
      "ICEBERG     &            0.727 &            0.699 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3077829/4028956387.py:30: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  latex = out_df_pivot_sorted.to_latex(\n"
     ]
    }
   ],
   "source": [
    "# only build scaffold split and normal split for NIST\n",
    "\n",
    "out_df_temp = out_df[out_df[\"Dataset\"].isin([\"nist20\", \"scaffold_nist20\"])]\n",
    "out_df_pivot = out_df_temp.pivot_table(\n",
    "    index=\"Method\",\n",
    "    columns=\"Dataset\",\n",
    "    values=[\n",
    "        \"Cosine sim. orig\",\n",
    "    ],\n",
    "    aggfunc=lambda x: x,\n",
    ")\n",
    "out_df_pivot_sorted = out_df_pivot.loc[names]\n",
    "\n",
    "out_df_pivot_sorted = out_df_pivot_sorted.swaplevel(0, 1, axis=1).round(3)\n",
    "\n",
    "metric_order = [\"Cosine sim. orig\"]\n",
    "dataset_order = [\"nist20\", \"scaffold_nist20\"]\n",
    "\n",
    "# Create a new MultiIndex with the custom sort order\n",
    "new_index = pd.MultiIndex.from_product(\n",
    "    [dataset_order, metric_order], names=[\"Dataset\", \"Metric\"]\n",
    ")\n",
    "\n",
    "out_df_pivot_sorted = out_df_pivot_sorted.loc[:, new_index]\n",
    "\n",
    "\n",
    "# Sort columns and make time last\n",
    "\n",
    "out_df_pivot_sorted.index.name = None\n",
    "latex = out_df_pivot_sorted.to_latex(\n",
    "    caption=\"Comparing NIST scaffold vs. random spllit cosine similarity\",\n",
    "    label=\"tab:scaffold\",\n",
    "    escape=False,\n",
    ")\n",
    "print(latex)\n",
    "\n",
    "# Output to file \"nist_scaffold_cosine.txt\" in result folder\n",
    "with open(results_folder / \"nist_scaffold_cosine.txt\", \"w\") as f:\n",
    "    f.write(latex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\\centering\n",
      "\\caption{Spectra prediction accuracy}\n",
      "\\label{tab:spec_acc}\n",
      "\\begin{tabular}{lllllllllll}\n",
      "\\toprule\n",
      "Dataset & \\multicolumn{3}{l}{canopus_train_public} & \\multicolumn{3}{l}{nist20} & \\multicolumn{4}{l}{scaffold_nist20} \\\\\n",
      "{} &          Cosine sim. &           Coverage &             Valid &        Cosine sim. &           Coverage &             Valid &        Cosine sim. &           Coverage &             Valid &  Time (s) \\\\\n",
      "\\midrule\n",
      "CFM-ID      &    $0.377 \\pm 0.000$ &  $0.235 \\pm 0.000$ &  $1.00 \\pm 0.000$ &  $0.412 \\pm 0.000$ &  $0.278 \\pm 0.000$ &  $1.00 \\pm 0.000$ &  $0.411 \\pm 0.000$ &  $0.272 \\pm 0.000$ &  $1.00 \\pm 0.000$ &  $1114.7$ \\\\\n",
      "3DMolMS     &    $0.394 \\pm 0.004$ &  $0.507 \\pm 0.001$ &  $0.92 \\pm 0.001$ &  $0.510 \\pm 0.001$ &  $0.734 \\pm 0.002$ &  $0.94 \\pm 0.002$ &  $0.466 \\pm 0.000$ &  $0.707 \\pm 0.000$ &  $0.97 \\pm 0.000$ &     $3.5$ \\\\\n",
      "FixedVocab  &    $0.568 \\pm 0.003$ &  $0.563 \\pm 0.002$ &  $1.00 \\pm 0.000$ &  $0.704 \\pm 0.001$ &  $0.788 \\pm 0.001$ &  $1.00 \\pm 0.000$ &  $0.658 \\pm 0.000$ &  $0.785 \\pm 0.000$ &  $1.00 \\pm 0.000$ &     $5.5$ \\\\\n",
      "NEIMS (FFN) &    $0.491 \\pm 0.003$ &  $0.524 \\pm 0.002$ &  $0.95 \\pm 0.001$ &  $0.617 \\pm 0.001$ &  $0.746 \\pm 0.002$ &  $0.95 \\pm 0.001$ &  $0.546 \\pm 0.000$ &  $0.717 \\pm 0.000$ &  $0.96 \\pm 0.000$ &     $3.9$ \\\\\n",
      "NEIMS (GNN) &    $0.521 \\pm 0.003$ &  $0.547 \\pm 0.005$ &  $0.94 \\pm 0.001$ &  $0.694 \\pm 0.001$ &  $0.780 \\pm 0.001$ &  $0.95 \\pm 0.001$ &  $0.643 \\pm 0.000$ &  $0.766 \\pm 0.000$ &  $0.97 \\pm 0.000$ &     $4.9$ \\\\\n",
      "MassFormer  &    $0.568 \\pm 0.004$ &  $0.573 \\pm 0.009$ &  $0.95 \\pm 0.003$ &  $0.721 \\pm 0.004$ &  $0.790 \\pm 0.006$ &  $0.93 \\pm 0.006$ &  $0.682 \\pm 0.000$ &  $0.789 \\pm 0.000$ &  $0.96 \\pm 0.000$ &     $7.7$ \\\\\n",
      "SCARF       &    $0.536 \\pm 0.013$ &  $0.552 \\pm 0.016$ &  $1.00 \\pm 0.000$ &  $0.726 \\pm 0.002$ &  $0.807 \\pm 0.001$ &  $1.00 \\pm 0.000$ &  $0.669 \\pm 0.000$ &  $0.807 \\pm 0.000$ &  $1.00 \\pm 0.000$ &    $21.1$ \\\\\n",
      "ICEBERG     &    $0.627 \\pm 0.002$ &  $0.549 \\pm 0.003$ &  $1.00 \\pm 0.000$ &  $0.727 \\pm 0.002$ &  $0.754 \\pm 0.002$ &  $1.00 \\pm 0.000$ &  $0.699 \\pm 0.000$ &  $0.771 \\pm 0.000$ &  $1.00 \\pm 0.000$ &    $82.2$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3077829/3614950898.py:34: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  latex = out_df_pivot_sorted.to_latex(\n"
     ]
    }
   ],
   "source": [
    "out_df_pivot = out_df.pivot_table(\n",
    "    index=\"Method\",\n",
    "    columns=\"Dataset\",\n",
    "    values=[\n",
    "        \"Cosine sim.\",\n",
    "        \"Coverage\",\n",
    "        \"Valid\",\n",
    "        \"Time (s)\",\n",
    "    ],\n",
    "    aggfunc=lambda x: x,\n",
    ")\n",
    "out_df_pivot_sorted = out_df_pivot.loc[names]\n",
    "\n",
    "out_df_pivot_sorted = out_df_pivot_sorted.swaplevel(0, 1, axis=1).round(3)\n",
    "\n",
    "\n",
    "metric_order = {\"Cosine sim.\": 1, \"Coverage\": 2, \"Valid\": 3, \"Time (s)\": 4}\n",
    "metric_order = [\"Cosine sim.\", \"Coverage\", \"Valid\", \"Time (s)\"]\n",
    "dataset_order = {\"nist20\": 2, \"canopus_train_public\": 1, \"scaffold_nist20\": 3}\n",
    "dataset_order = [\"canopus_train_public\", \"nist20\", \"scaffold_nist20\"]\n",
    "\n",
    "# Create a new MultiIndex with the custom sort order\n",
    "new_index = pd.MultiIndex.from_product(\n",
    "    [dataset_order, metric_order], names=[\"Dataset\", \"Metric\"]\n",
    ")\n",
    "\n",
    "out_df_pivot_sorted = out_df_pivot_sorted.loc[:, new_index]\n",
    "out_df_pivot_sorted = out_df_pivot_sorted.drop(\n",
    "    columns=[(\"canopus_train_public\", \"Time (s)\"), (\"nist20\", \"Time (s)\")]\n",
    ")\n",
    "\n",
    "# Sort columns and make time last\n",
    "out_df_pivot_sorted.index.name = None\n",
    "latex = out_df_pivot_sorted.to_latex(\n",
    "    caption=\"Spectra prediction accuracy\", label=\"tab:spec_acc\", escape=False\n",
    ")\n",
    "print(latex)\n",
    "\n",
    "\n",
    "# Output to file \"all_cosine.txt\" in result folder\n",
    "with open(results_folder / \"all_cosine.txt\", \"w\") as f:\n",
    "    f.write(latex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.46464646464645"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdiff = lambda x,y: abs(x-y)*100/y\n",
    "pdiff(0.63, 0.57)\n",
    "pdiff(0.29, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\\centering\n",
      "\\caption{Spectra prediction accuracy}\n",
      "\\label{tab:spec_acc_nist_all}\n",
      "\\begin{tabular}{llllllll}\n",
      "\\toprule\n",
      "Dataset & \\multicolumn{3}{l}{nist20} & \\multicolumn{4}{l}{scaffold_nist20} \\\\\n",
      "{} &        Cosine sim. &           Coverage &             Valid &        Cosine sim. &           Coverage &             Valid &  Time (s) \\\\\n",
      "\\midrule\n",
      "CFM-ID      &  $0.412 \\pm 0.000$ &  $0.278 \\pm 0.000$ &  $1.00 \\pm 0.000$ &  $0.411 \\pm 0.000$ &  $0.272 \\pm 0.000$ &  $1.00 \\pm 0.000$ &  $1114.7$ \\\\\n",
      "3DMolMS     &  $0.510 \\pm 0.001$ &  $0.734 \\pm 0.002$ &  $0.94 \\pm 0.002$ &  $0.466 \\pm 0.000$ &  $0.707 \\pm 0.000$ &  $0.97 \\pm 0.000$ &     $3.5$ \\\\\n",
      "FixedVocab  &  $0.704 \\pm 0.001$ &  $0.788 \\pm 0.001$ &  $1.00 \\pm 0.000$ &  $0.658 \\pm 0.000$ &  $0.785 \\pm 0.000$ &  $1.00 \\pm 0.000$ &     $5.5$ \\\\\n",
      "NEIMS (FFN) &  $0.617 \\pm 0.001$ &  $0.746 \\pm 0.002$ &  $0.95 \\pm 0.001$ &  $0.546 \\pm 0.000$ &  $0.717 \\pm 0.000$ &  $0.96 \\pm 0.000$ &     $3.9$ \\\\\n",
      "NEIMS (GNN) &  $0.694 \\pm 0.001$ &  $0.780 \\pm 0.001$ &  $0.95 \\pm 0.001$ &  $0.643 \\pm 0.000$ &  $0.766 \\pm 0.000$ &  $0.97 \\pm 0.000$ &     $4.9$ \\\\\n",
      "MassFormer  &  $0.721 \\pm 0.004$ &  $0.790 \\pm 0.006$ &  $0.93 \\pm 0.006$ &  $0.682 \\pm 0.000$ &  $0.789 \\pm 0.000$ &  $0.96 \\pm 0.000$ &     $7.7$ \\\\\n",
      "SCARF       &  $0.726 \\pm 0.002$ &  $0.807 \\pm 0.001$ &  $1.00 \\pm 0.000$ &  $0.669 \\pm 0.000$ &  $0.807 \\pm 0.000$ &  $1.00 \\pm 0.000$ &    $21.1$ \\\\\n",
      "ICEBERG     &  $0.727 \\pm 0.002$ &  $0.754 \\pm 0.002$ &  $1.00 \\pm 0.000$ &  $0.699 \\pm 0.000$ &  $0.771 \\pm 0.000$ &  $1.00 \\pm 0.000$ &    $82.2$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3077829/2482128940.py:4: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  latex = out_df_pivot_sorted_temp.to_latex(\n"
     ]
    }
   ],
   "source": [
    "out_df_pivot_sorted_temp = out_df_pivot_sorted.copy().drop(\n",
    "    columns=[(\"canopus_train_public\")]\n",
    ")\n",
    "latex = out_df_pivot_sorted_temp.to_latex(\n",
    "    caption=\"Spectra prediction accuracy\", label=\"tab:spec_acc_nist_all\", escape=False\n",
    ")\n",
    "print(latex)\n",
    "\n",
    "\n",
    "# Output to file \"all_cosine.txt\" in result folder\n",
    "with open(results_folder / \"nist_cosine.txt\", \"w\") as f:\n",
    "    f.write(latex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\\centering\n",
      "\\caption{Spectra prediction accuracy}\n",
      "\\label{tab:spec_acc_gnps}\n",
      "\\begin{tabular}{llll}\n",
      "\\toprule\n",
      "Dataset & \\multicolumn{3}{l}{canopus_train_public} \\\\\n",
      "{} &          Cosine sim. &           Coverage &             Valid \\\\\n",
      "\\midrule\n",
      "CFM-ID      &    $0.377 \\pm 0.000$ &  $0.235 \\pm 0.000$ &  $1.00 \\pm 0.000$ \\\\\n",
      "3DMolMS     &    $0.394 \\pm 0.004$ &  $0.507 \\pm 0.001$ &  $0.92 \\pm 0.001$ \\\\\n",
      "FixedVocab  &    $0.568 \\pm 0.003$ &  $0.563 \\pm 0.002$ &  $1.00 \\pm 0.000$ \\\\\n",
      "NEIMS (FFN) &    $0.491 \\pm 0.003$ &  $0.524 \\pm 0.002$ &  $0.95 \\pm 0.001$ \\\\\n",
      "NEIMS (GNN) &    $0.521 \\pm 0.003$ &  $0.547 \\pm 0.005$ &  $0.94 \\pm 0.001$ \\\\\n",
      "MassFormer  &    $0.568 \\pm 0.004$ &  $0.573 \\pm 0.009$ &  $0.95 \\pm 0.003$ \\\\\n",
      "SCARF       &    $0.536 \\pm 0.013$ &  $0.552 \\pm 0.016$ &  $1.00 \\pm 0.000$ \\\\\n",
      "ICEBERG     &    $0.627 \\pm 0.002$ &  $0.549 \\pm 0.003$ &  $1.00 \\pm 0.000$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3077829/612728215.py:4: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  latex = out_df_pivot_sorted_temp.to_latex(\n"
     ]
    }
   ],
   "source": [
    "out_df_pivot_sorted_temp = out_df_pivot_sorted.copy().drop(\n",
    "    columns=[\"scaffold_nist20\", \"nist20\"]\n",
    ")\n",
    "latex = out_df_pivot_sorted_temp.to_latex(\n",
    "    caption=\"Spectra prediction accuracy\", label=\"tab:spec_acc_gnps\", escape=False\n",
    ")\n",
    "print(latex)\n",
    "\n",
    "# Output to file \"all_cosine.txt\" in result folder\n",
    "with open(results_folder / \"gnps_cosine_only.txt\", \"w\") as f:\n",
    "    f.write(latex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Random': '#808080',\n",
       " 'CFM-ID': '#C2AF53',\n",
       " '3DMolMS': '#A56F8F',\n",
       " 'MassFormer': '#4C0027',\n",
       " 'FixedVocab': '#41644A',\n",
       " 'NEIMS (FFN)': '#B94346',\n",
       " 'NEIMS (GNN)': '#DB8F76',\n",
       " 'SCARF': '#479394',\n",
       " 'ICEBERG': '#3D4A9F'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3077829/179192944.py:31: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(method_names, rotation=45, ha=\"right\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'CFM-ID'),\n",
       " Text(1, 0, '3DMolMS'),\n",
       " Text(2, 0, 'FixedVocab'),\n",
       " Text(3, 0, 'NEIMS (FFN)'),\n",
       " Text(4, 0, 'NEIMS (GNN)'),\n",
       " Text(5, 0, 'MassFormer'),\n",
       " Text(6, 0, 'SCARF'),\n",
       " Text(7, 0, 'ICEBERG')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Define Cosine sim bar plots\n",
    "\n",
    "dataset = \"nist20\"\n",
    "dataset_sub = out_df[[\"Method\", \"Cosine sim. orig\", \"Cosine sim. 95%\", \"Dataset\"]].query(\n",
    "    f\"Dataset == '{dataset}'\"\n",
    ")\n",
    "dataset_sub.set_index(\"Method\", inplace=True)\n",
    "fig = plt.figure(figsize=(3, 1), dpi=400)\n",
    "\n",
    "ax = fig.gca()\n",
    "dataset_sub = dataset_sub.loc[names]\n",
    "sim = dataset_sub[[\"Cosine sim. orig\"]].values.flatten()\n",
    "errors = dataset_sub[[\"Cosine sim. 95%\"]].values.flatten()\n",
    "method_names = dataset_sub.index.values\n",
    "\n",
    "ax.bar(method_names, sim, color=[method_colors[i] for i in method_names], \n",
    "    # errors\n",
    "    yerr=errors,\n",
    "    # Add style to make them visible\n",
    "    capsize=2,\n",
    "    \n",
    ")\n",
    "\n",
    "# Add 95% conf bars\n",
    "\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"Cosine sim. avg\")\n",
    "ax.set_xlabel(\"Method\")\n",
    "# Rotate x tick labels\n",
    "ax.set_xticklabels(method_names, rotation=45, ha=\"right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3077829/727644166.py:42: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(method_names, rotation=90, ha=\"center\")\n",
      "/tmp/ipykernel_3077829/727644166.py:42: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(method_names, rotation=90, ha=\"center\")\n",
      "/tmp/ipykernel_3077829/727644166.py:76: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(method_names, rotation=90, ha=\"center\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1114.6517303     3.4905448     5.51927352    3.90746284    4.92101884\n",
      "    7.70103574   21.06114841   82.24074769]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1480x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot cosine similarities and time for 2 datasets\n",
    "# define subplots\n",
    "gridspec = dict(hspace=0.0, width_ratios=[1, 1, 0.45, 1])\n",
    "fig, axes = plt.subplots(\n",
    "    1, 4, figsize=(3.7, 1.2), dpi=400, sharey=False, gridspec_kw=gridspec\n",
    ")\n",
    "plt_dataset_names = {\"canopus_train_public\": \"NPLIB1\", \"nist20\": \"NIST20\"}\n",
    "\n",
    "plot_datasets = [\n",
    "    \"canopus_train_public\",\n",
    "    \"nist20\",\n",
    "]\n",
    "for ind, (ax, dataset) in enumerate(zip(axes, plot_datasets)):\n",
    "\n",
    "    dataset_sub = out_df[\n",
    "        [\"Method\", \"Cosine sim. orig\", \"Cosine sim. 95%\", \"Dataset\"]\n",
    "    ].query(f\"Dataset == '{dataset}'\")\n",
    "    dataset_sub.set_index(\"Method\", inplace=True)\n",
    "    dataset_sub = dataset_sub.loc[names]\n",
    "    sim = dataset_sub[[\"Cosine sim. orig\"]].values.flatten()\n",
    "    sem = dataset_sub[[\"Cosine sim. 95%\"]].values.flatten()\n",
    "\n",
    "    # Using sem compute 95% confidence interval and plot error bars\n",
    "    ci = sem * 1.96\n",
    "\n",
    "    method_names = dataset_sub.index.values\n",
    "\n",
    "    ax.bar(\n",
    "        method_names,\n",
    "        sim,\n",
    "        color=[method_colors[i] for i in method_names],\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.5,\n",
    "        yerr=ci,\n",
    "        capsize=1.5,\n",
    "        error_kw=dict(lw=0.5, capsize=1.5, capthick=0.5),\n",
    "    )\n",
    "    if ind == 0:\n",
    "        ax.set_ylabel(\"Cosine sim.\")\n",
    "    # ax.set_xlabel(\"Method\")\n",
    "    # Rotate x tick labels\n",
    "    ax.set_xticklabels(method_names, rotation=90, ha=\"center\")\n",
    "    ax.set_title(plt_dataset_names.get(dataset))\n",
    "    ax.yaxis.labelpad = 1\n",
    "\n",
    "axes[0].get_shared_y_axes().join(axes[0], axes[1])\n",
    "axes[1].set_yticklabels([])\n",
    "axes[2].set_visible(False)\n",
    "\n",
    "ax = axes[3]\n",
    "dataset_sub = out_df[[\"Method\", \"Dataset\", \"Time (s) orig\"]].query(\n",
    "    f\"Dataset == '{dataset}'\"\n",
    ")\n",
    "dataset_sub.set_index(\"Method\", inplace=True)\n",
    "dataset_sub = dataset_sub.loc[names]\n",
    "time = dataset_sub[[\"Time (s) orig\"]].values.flatten()\n",
    "print(time)\n",
    "method_names = dataset_sub.index.values\n",
    "\n",
    "ax.bar(\n",
    "    method_names,\n",
    "    time,\n",
    "    color=[method_colors[i] for i in method_names],\n",
    "    edgecolor=\"black\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "\n",
    "# Remove padding from  y label\n",
    "ax.set_ylabel(\n",
    "    \"Time (s)\",\n",
    ")\n",
    "ax.yaxis.labelpad = 1\n",
    "ax.set_yscale(\"log\")\n",
    "# ax.set_xlabel(\"Method\")\n",
    "# Rotate x tick labels\n",
    "ax.set_xticklabels(method_names, rotation=90, ha=\"center\")\n",
    "\n",
    "# Remove y minor ticks\n",
    "ax.yaxis.set_minor_locator(plt.NullLocator())\n",
    "\n",
    "# Space 3rd plot further from others\n",
    "# fig.tight_layout()\n",
    "\n",
    "\n",
    "# Save fig to file\n",
    "fig.savefig(\n",
    "    results_folder / \"cosine_time_barplot.pdf\", bbox_inches=\"tight\", transparent=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# out_df = out_df.set_index([\"Method\", \"Dataset\"]).round(3)\n",
    "# out_df.index.name = None\n",
    "# latex = out_df.to_latex(caption=\"Spectra prediction accuracy\", label=\"tab:spec_acc\")\n",
    "# print(latex)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the differences between datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Randomly sample 10 molecules and draw them\n",
    "import random\n",
    "\n",
    "labels_canopus = \"../data/spec_datasets/canopus_train_public/labels.tsv\"\n",
    "labels_nist = \"../data/spec_datasets/nist20/labels.tsv\"\n",
    "\n",
    "df_1 = pd.read_csv(labels_canopus, sep=\"\\t\")\n",
    "df_2 = pd.read_csv(labels_nist, sep=\"\\t\")\n",
    "\n",
    "for data_name, df in zip([\"canopus\", \"nist\"], [df_1, df_2]):\n",
    "    sub_folder = results_folder / f\"mol_examples/{data_name}\"\n",
    "    sub_folder.parent.mkdir(exist_ok=True)\n",
    "    sub_folder.mkdir(exist_ok=True)\n",
    "\n",
    "    random.seed(2)\n",
    "    np.random.seed(2)\n",
    "\n",
    "    with open(sub_folder / f\"mol_smis.txt\", \"w\") as fp:\n",
    "        for i in range(10):\n",
    "            smi = random.choice(df[\"smiles\"].values)\n",
    "            mol = Chem.MolFromSmiles(smi)\n",
    "            img = Draw.MolToImage(mol, size=(200, 200))\n",
    "            img.save(sub_folder / f\"mol_{i}.png\")\n",
    "            fp.write(f\"{smi}\\t{i}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...Found local copy...\n",
      "\n",
      "Found local copy...\n",
      "Found local copy...Found local copy...\n",
      "\n",
      "Found local copy...\n",
      "Found local copy...Found local copy...\n",
      "\n",
      "Found local copy...Found local copy...\n",
      "\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...Found local copy...\n",
      "\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...Found local copy...\n",
      "\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n",
      "Found local copy...\n"
     ]
    }
   ],
   "source": [
    "import tdc\n",
    "from rdkit.Chem import Descriptors\n",
    "\n",
    "oracle = tdc.oracles.Oracle(name=\"SA\")\n",
    "from ms_pred import common\n",
    "\n",
    "\n",
    "def calc_molweight(smiles):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    return Descriptors.MolWt(mol)\n",
    "\n",
    "\n",
    "name_to_weights = {}\n",
    "for data_name, df in zip([\"canopus\", \"nist\"], [df_1, df_2]):\n",
    "    mol_weights = common.chunked_parallel(df[\"smiles\"].values, calc_molweight)\n",
    "    name_to_weights[data_name] = mol_weights\n",
    "\n",
    "\n",
    "name_to_sa = {}\n",
    "for data_name, df in zip([\"canopus\", \"nist\"], [df_1, df_2]):\n",
    "    sa_score = common.chunked_parallel(df[\"smiles\"].values, lambda x: oracle(x))\n",
    "    name_to_sa[data_name] = sa_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'canopus': 3.7522641916419297, 'nist': 3.014947602506307}\n",
      "{'canopus': 412.7486759734804, 'nist': 316.8960129237952}\n"
     ]
    }
   ],
   "source": [
    "print({i: np.mean(j) for i, j in name_to_sa.items()})\n",
    "print({i: np.mean(j) for i, j in name_to_weights.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1238.71x481.06 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distributions of SA score and molecular weight\n",
    "# define subplots\n",
    "gridspec = dict(hspace=0.0, width_ratios=[1, 0.1, 1])\n",
    "fig, axes = plt.subplots(\n",
    "    1, 3, figsize=(3.8, 1.2), dpi=400, sharey=False, gridspec_kw=gridspec\n",
    ")\n",
    "plt_dataset_names = {\"canopus\": \"NPLIB1\", \"nist\": \"NIST20\"}\n",
    "\n",
    "dataset_colors = {\"nist\": \"#7369AF\", \"canopus\": \"#BF5A52\"}\n",
    "alpha = 0.6\n",
    "ax = axes[0]\n",
    "\n",
    "for plt_dataset_name in plt_dataset_names:\n",
    "    ax.hist(\n",
    "        name_to_sa[plt_dataset_name],\n",
    "        bins=20,\n",
    "        density=True,\n",
    "        color=dataset_colors[plt_dataset_name],\n",
    "        label=plt_dataset_names[plt_dataset_name],\n",
    "        alpha=alpha,\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.3,\n",
    "    )\n",
    "ax.set_xlabel(\"SA score\")\n",
    "ax.set_ylabel(\"Frequency\")\n",
    "ax.legend(**legend_params)\n",
    "\n",
    "\n",
    "axes[1].set_visible(False)\n",
    "\n",
    "ax = axes[2]\n",
    "for plt_dataset_name in plt_dataset_names:\n",
    "    ax.hist(\n",
    "        name_to_weights[plt_dataset_name],\n",
    "        bins=20,\n",
    "        density=True,\n",
    "        color=dataset_colors[plt_dataset_name],\n",
    "        label=plt_dataset_names[plt_dataset_name],\n",
    "        alpha=alpha,\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.3,\n",
    "    )\n",
    "ax.set_xlabel(\"Mol. weight\")\n",
    "# ax.set_ylabel(\"Frequency\")\n",
    "ax.legend(**legend_params)\n",
    "set_size(2.4, 0.908, ax)\n",
    "\n",
    "fig.savefig(\n",
    "    results_folder / \"sa_molweight_hist.pdf\", bbox_inches=\"tight\", transparent=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ms-gen",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
